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32 results for “cloud radar”
Simultaneous lidar and radar measurements for aeroso-cloud interaction studies
<p>Optical and microphysical aerosol and cloud properties derived from lidar and radar for aeroso-cloud interaction studies.</p>
Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight"
<p>Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight". The cloud radar data files contains all the data used in the publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight" by Charlotte E. Wainwright, Sabrina N. Volponi, Phillip M. Stepanian, Don R. Reynolds, and David H. Richter, published in Methods in Ecology and Evolution in 2022. The MATLAB code implements the method described in the paper on the data files included.</p>
Test Input and Output Files for Cloud Resolving Radar Simulator (CR-SIM) Version 4.0
<h2>Overview</h2> <p>The dataset includes input and output files for testing the Cloud-Resolving Radar Simulator (Oue et al. 2020) version 4.0. </p> <p>The following files are included:</p> <ul> <li>crsimtest1_inp_MP10.tar.gz includes input files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_inp_MP50.tar.gz includes input files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_inp_MP40.tar.gz includes input files for Test-3 with the microphysical option MP40</li> <li>crsimtest1_out_ref_MP10.tar.gz includes example output files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_out_ref _MP50.tar.gz includes example output files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_out_ref _MP40.tar.gz includes example output files for Test-3 with the microphysical option MP40</li> </ul> <p>Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v4.0).</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) - KITcube cloud radar vertical winds in Kolsass
<p>The RPG FMCW dual-pol dual-frequency cloud radar was operated in Kolsass. This data set contains vertical wind speed in 10 s temporal resolution for both frequencies, 94 GHz and 35 GHz. Vertical wind measurements are interrupted by PPI scans, thus there are regular gaps. The data set covers the period from May, 18th, through Sept., 22th, 2022.There were technical problems which caused partly very long measurement gaps especially in the second half of the period. Data are stored as one NetCDF file.<br> </p>
TEAMx-PC22 (TEAMx pre-campaing 2022) - KITcube cloud radar horizontal winds (from PPI) in Kolsass
<p>The RPG FMCW dual-pol dual-frequency cloud radar was operated in Kolsass. This data set contains wind speed and wind direction in 10 min. temporal resolution for both frequencies, 94 GHz and 35 GHz. The wind is determined from PPI at 70 degree elevation via an unfolding VAD algorithm (see Pierre Tabary, Georges Scialom, and Urs Germann. Real-time retrieval of the wind from aliased velocities measured by doppler radars. J. Atmos. Oceanic Technol., 18 (6):875–882, June 2001.) PPIs were performed from May, 31st, through Aug, 26th, 2022. There were technical problems which caused partly very long measurement gaps especially in the second half of the period. Data are stored as one NetCDF file.<br> </p>
Dataset for mammatus-like radar echoes along the bases of upper-tropospheric outflow-layer clouds of typhoons.
<p>PPI and RHI data of radar observations of mammatus-like echoes at the cloud bases of upper-level clouds of typhoons observed by the Nagoya-University cloud radar. The observations were carried out at Okinawa, Japan in 2016 and 2019, and Kobe, Japan in 2018 This dataset includes raw data at polar coordinates and grid data interpolated to Cartesian coordinates. This dataset also includes upper-level sounding observation data during the appearance of mammatus-like echoes at Okinawa, Japan in 2016.</p> <p>In this version 2, all RHI data obtained in 3 October 2016 were added.</p>
Cloud radar, micro rain radar, parsivel and pluvio measurements at Ny-Ålesund for 7 Feb 2018, 16 March 2018 and 16 April 2018
<p>This data set contains netcdf files of the University of Cologne's cloud radar MiRAC-A, micro rain radar, parsivel and pluvio installed at the Arctic research site AWPIEV at Ny-Ålesund. Data are available for 3 days: 7 Feb 2018, 16 March 2018 and 16 April 2018.</p> <p>The MiRAC-A hourly files (“mirac-a_nya_compact_*_P01_ZEN.nc” include (among other variables):</p> <p>float Ze(time, range) ;</p> <p>Ze:long_name = "Equivalent radar reflectivity factor Ze" ;</p> <p>Ze:units = "mm^6/m^3" ;</p> <p>float vm(time, range) ;</p> <p>vm:long_name = "Mean Doppler velocity" ;</p> <p>vm:units = "m/s" ;</p> <p>vm:comment = "negative values indicate falling particles towards the radar" ;</p> <p>float sigma(time, range) ;</p> <p>sigma:long_name = "Spectral width of Doppler velocity spectrum" ;</p> <p>sigma:units = "m/s" ;</p> <p> </p> <p>The Micro Rain Radar daily files (“*_nya_mrr_improtoo_0-101.nc”) include (among other variables):</p> <p>float Ze(time, range) ;</p> <p>Ze:description = "reflectivity of the most significant peak" ;</p> <p>Ze:units = "dBz" ;</p> <p> </p> <p>The parsivel daily files (“sups_nya_dm00_l1_any_v00_*.nc”) include (among other variables):</p> <p>float N(dclasses, time) ;</p> <p>N:fill_value = NaN ;</p> <p>N:units = "log10(m-3 mm-1)" ;</p> <p>N:long_name = "particle concentration per diameter class" ;</p> <p>float dclasses(dclasses) ;</p> <p>dclasses:units = "mm" ;</p> <p>dclasses:long_name = "volume equivalent diameter class center" ;</p> <p> </p> <p>The pluvio daily files (“pluvio_nya_*.nc”) include (among other variables):</p> <p>r_accum_NRT(dim) ;</p> <p>r_accum_NRT:description = "accumulated precipitation NRT" ;</p> <p>r_accum_NRT:units = "mm" ;</p> <p> </p> <p>All available variables are listed and described in the header of the corresponding netcdf files.</p>
94 GHz cloud radar simulation data using NICAM and Joint simulator for evaluation of ground-based radar and application to the EarthCARE satellite
<p><strong>Overview</strong></p> <p>These data are snapshots of simulated radar reflectivity and Doppler velocity from the 94 GHz Cloud Profiling Radar (CPR). The simulations were conducted using the Joint-Simulator for Satellite Sensors (Joint-Simulator; Hashino et al., 2013; Roh et al., 2020), and the input data was from the Nonhydorstatic Icosahedral Atmospheric Model (NICAM; Satoh et al., 2014). To focus on the region of interest with high resolution, NICAM transformed the grid (the stretched NICAM; Tomita 2008a) using a G-Level 10 (GL10) horizontal resolution with a stretch-factor of 100 (the ratio between the maximum and minimum grid intervals), where the minimum grid interval is approximately 800 m. We simulated two cases of rain events in September 2019. The first case (case 1) is the tropical cyclone (TC) Faxai. The second is a weak frontal system (case 2). In case 1 the integration and analysis time was from 00 UTC on 8 September to 00 UTC on 9 September 2019. In case 2 the integration and analysis time was from 00 UTC on 20 September to 00 UTC on 21 September 2019. We evaluated the data using the gournd observation and introduced a methodology for using the CPR data for model evaluations. We simulated Doppler velocity of the EarthCARE CPR. </p> <p> </p> <p><strong>Directory structure and file format</strong></p> <p>There are two files.</p> <p>For Ground_CPR, there are simuation data based on the ground.</p> <p>For Satellite_CPR, there are simulation data bsed on instrument setting of EarthCARE CPR.</p> <p>(Note the order of the array of Satelltie_CPR is different from Ground_CPR.)</p> <p>The files are in the NetCDF4 format.</p> <p>The files have the following name format.</p> <p>AAA_XXX_TIME_EASE.nc</p> <p>AAA: Ecare (CPR simulation withot random errors), Mode(CPR simulation with randome errors based on window observaion mode)</p> <p>XXX: NICAM Single Moment sheme 6 category (NSW6) and NICAM Dobule Moment scheme 6 category(NDW6)</p> <p>TIME: The simulation time</p> <p>EASE: EarthCARE Active SEnsor simulator (EASE)</p> <p> </p> <p> </p> <p> </p> <p>These data are only one snapshot data and limited variables becuase of file size issue.</p> <p>If you have any questions or want additional data, please contact the email below</p> <p>Contacts:</p> <p>Woosub Roh (ws-roh@aori.u-tokyo.ac.jp)</p>
Ka, W and G-band radar observations of clouds and light precipitation during the EPCAPE campaign in March and April 2023
<p>The files contained in the data sets include Ka, W and G-band radar observations of clouds and light precipitation for several days from March 23 to April 27, 2023, during the EPCAPE campaign in La Jolla, CA, USA. <br>The YYYYMMDD_HHMMSS file naming convention corresponds to the starting measurement time of the data set in UTC.<br>The CloudCube_EPCAPE_Gband_Spectra.zip folder contains G-band radar Doppler spectra in the form of calibrated reflectivity as a function of Doppler velocity and range, where the noise has been masked out. The CloudCube_EPCAPE_Gband_Spectra_Noise.zip folder contains G-band radar Doppler spectra, including noise and SNR. The CloudCube_EPCAPE_Gband_Moments.zip folder contains G-band radar Doppler spectra moments, i.e. calibrated reflectivity, mean Doppler velocity and Doppler spectrum width. The CloudCube_EPCAPE_Multifrequency.zip folder includes Ka, W and G-band calibrated reflectivity and dual-frequency reflectivity ratios. <br>These data were obtained from CloudCube, a Ka, W and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals.<br>For more details about the data processing and description, please refer to: Socuellamos, J. M., Rodriguez Monje, R., Lebsock, M. D., Cooper, K. B., Beauchamp, R. M., and Umeyama, A.: Multifrequency radar observations of marine clouds during the EPCAPE campaign, Earth Syst. Sci. Data. https://doi.org/10.5194/essd-2023-454, 2024. </p>
TRIPEx-pol dataset containing LV2 cloud radar data and polarimetric radar data
<p>This dataset contains all data used for the publication von Terzi et al. 2022, ACP: "Ice microphysical processes in the dendritic growth layer: A statistical analysis combining multi-frequency and polarimetric Doppler cloud radar observations". This dataset combines the observations from vertically pointing X-, Ka- and W-Band radars and observations from a polarimetric W-Band radar pointing at 30° elevation.The dataset also contains variables derived from Doppler spectra observations: The spectral Edge velocity derived from Ka-Band vertically pointing radar and the maximum of the spectral ZDR (sZDRmax) from the polarimetric W-Band radar at 30° elevation. The data where averaged within periods when the polarimetric W-Band radar was measuring at 30° elevation. This corresponds to approximately 5 minute periods. The non-averaged LV2 data from the vertically pointing X-, Ka- and W-band radars is further available at: 10.5281/zenodo.5025636</p>
BSWO_cloud radar data
<p>Cloud radar data observed at BSWO between 2014 and 2017.</p> <p>cfradial files are identical to the netCDF files. </p> <p> </p>
The mmWave radar point cloud dataset for person identification under various occluded conditions
<p>The dataset is collected by a COTS, Freqency Modulated Continuous Wave radar and an RGB-D camera. The dataset has been collected from 9 recruited individuals, which are instructed to walk behind the obstacle in an inbound/outbound manner, with each subject perfoms 5 consecutive minutes of walking. The RGB dataset is accessible on https://zenodo.org/record/8401329</p>
Dataset for "On the quantification of thermodynamic phases of raining clouds: insights from multi-year CloudSat and ground-based radar observations over Longmen, Southern China"
Open the record for dataset details and reuse information.
AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC
This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project.The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information,and develop a merged, multi-sensor climatology of atmospheric water vapor as afunction of altitude, stratified by the cloud classes. This is a large scienceanalysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, findspace/time "matchups" between pairs of instruments, and process years ofsatellite data to produce the climate data records.The short name for this collection is AIRSM_CPR_MATParameters contained in the data files include the following:Variable Name|Description|Units CH4_total_column|Retrieved total column CH4| (molecules/cm2) CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None) CloudLayers| Number of hydrometeor layers| (count) clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2) CO_total_column|Retrieved total column CO| (molecules/cm2) CPR_Cloud_mask| CPR Cloud Mask |(None) Data_quality| Data Quality |(None) H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg) H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air) MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None) MODIS_scene_var |MODIS scene variability| (None) nSurfStd|1-based index of the first valid level|(None) O3VMRStd|Ozone Volume Mixing Ratio|(vmr) olr|All-sky Outgoing Longwave Radiation|(Watts/m**2) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) Sigma-Zero| Sigma-Zero| (dB*100) TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K) TCldTopStd|Cloud top temperature|(K) totH2OStd|Total precipitable water vapor| (kg/m**2) totO3Std|Total ozone burden| (Dobson) TSurfAir|Atmospheric Temperature at Surface|(K) TSurfStd|Surface skin temperature|(K)End of parameter information
TCSP CLOUD RADAR SYSTEM (CRS) V1
The TCSP Cloud Radar System (CRS) datasets consists of vertically profiled reflectivity and Doppler velocity at aircraft nadir along the flight track. The CRS is a 94 GHz (W-band; 3 mm wavelength) Doppler radar developed for autonomous operation in the NASA ER-2 high-altitude aircraft and for ground-based operation. It provided high-resolution profiles of reflectivity and Doppler velocity in clouds and it has important applications to atmospheric remote sensing studies. The CRS was designed to fly with the Cloud Lidar System (CLS), in the tail cone of an ER-2 superpod. There are two basic modes of operation of the CRS: 1) ER-2 with reflectivity, Doppler, and linear-depolarization measurements, and 2) ground-based with full polarimetric capability. The Tropical Cloud Systems and Processes (TCSP) mission used the ER-2 mode. The TCSP mission collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind and air pressure that creates ideal birthing conditions for tropical storms, hurricanes and related phenomena. The goal of this mission was to help us better understand how hurricanes and other tropical storms are formed and intensify.
GOES-R PLT Cloud Radar System (CRS)
The GOES-R PLT Field Campaign Cloud Radar System (CRS) dataset provides high-resolution profiles of reflectivity and Doppler velocity at aircraft nadir along the flight track. The CRS was flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign took place from March 21 to May 17, 2017 in support of post-launch product validation of the Advanced Baseline Image (ABI) and the Geostationary Lightning Mapper (GLM) aboard the GOES-R, now GOES-16, satellite. The CRS data files are available in netCDF-3 format with browse imagery available in PNG format.
GPM GROUND VALIDATION ACHIEVE W-BAND CLOUD RADAR IPHEX V1
The GPM Ground Validation ACHIEVE W-band Cloud Radar IPHEx dataset consists of cloud and light precipitation radar observations gathered during the Global Precipitation Measurement (GPM) mission Ground Validation Integrated Precipitation and Hydrology Experiment (IPHEx) Intensive Observing Period (IOP) in North Carolina from May 1 through June 15, 2014. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. The dataset includes data from the ProSensing 95 GHz W-band cloud radar, which is part of the NASA Goddard Space Flight Center (GSFC) Aerosol, Cloud, Humidity, Interactions Exploring and Validating Enterprise (ACHIEVE) ground-based mobile laboratory. The W-band cloud radar is a scanning 95 GHz dual-polarization (horizontal transmission and co- and cross-polar receiving) Doppler radar used for observing liquid and ice clouds and light precipitation. The instrument measures co- and cross-polar reflectivity, radial velocity, Doppler spectrum width, and signal-to-noise ratio. Linear depolarization ratio was derived from the measured parameters. During the IPHEx campaign, the W-Band radar was used exclusively in vertical-pointing mode. The dataset files are available from May 9 through June 14, 2014 in netCDF-3 data format.
ACHIEVE W-Band Cloud Radar IMPACTS
The ACHIEVE W-Band Cloud Radar IMPACTS dataset consists of reflectivity, signal-to-noise ratio, and radial velocity data measured during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. ACHIEVE W-Band Cloud Radar data are available from January 23, 2023, through March 1, 2023, in netCDF-4 format.
Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1
This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands.
GPM GROUND VALIDATION CLOUD RADAR SYSTEM (CRS) IPHEx V1
The GPM Ground Validation Cloud Radar System (CRS) IPHEx data were collected in support of the Global Precipitation Measurement (GPM) mission Integrated Precipitation and Hydrology Experiment (IPHEx) in North Carolina, with an intense study period occurring from May 1, 2014 through June 15, 2014. The goal of IPHEx was to evaluate the accuracy of satellite precipitation measurements and use the collected data for hydrology models in the region. The ER-2 aircraft flew during the IPHEx field campaign to aid in GPM validation. The science instruments, including the CRS, onboard the aircraft acted as a proxy for GPM satellite instruments. The CRS provided high-resolution profiles of reflectivity and Doppler velocity in clouds at aircraft nadir along the flight track. The CRS data are available from May 3, 2014 through June 12, 2014 and files for this dataset are available in netCDF-3 format.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.